stefanzhang / twitterrealtimeanalysis-dashboard Goto Github PK

This project is the FRONT END analysis implementation for Twitter real time sentiment analysis pipeline, which uses Apache Flume and Apache Spark. Final Project for WPI 2020 Fall CS573 Data Visualization.

• About the Project
  • Built With
• Getting Started
  • Installation
• Charts
• Complete Tasks
• Contributing
• Schedule of Deliverables
• License
• Contact
• Acknowledgements

Information is crucial to the function of a democratic society where well- informed citizens can make rational political decisions. While in the past political entities were primarily utilizing newspaper and later television to inform the public, with the rise of the Internet and online social media, the political arena has transformed into a more complex structure. So, it is essential to see how people on the Internet, particularly on Twitter, think about the presidential election candidates.

• CSS, HTML, Javascript
• PHP-5.6
• MySQL-5.6.49
• VegaLite-4.16.8
• PhpStorm

Make sure you have tweets data pipeline setup like this. The tweets are saved in the MySQL database and formated correctly.

1. Clone the repo into a host

git clone https://github.com/StefanZhang/TwitterRealTimeAnalysis-Dashboard.git

2. Export data from MySQL into .csv format using export_csv.php or SQL queries, and put under main directory.

3. End result: Dashboard

This Pie Chart displays the percentage of different sentiment respect to each other. The Majority of the tweets about Trump is negative.

This Aria Chart displays the number of the tweets posted respect to the time of a day.

This Line Chart displays the number of the tweets posted respect to date.

This Bar Chart displays the number of the tweets posted respect to device.

• Configured web environment.
• MySQL is appropriately setup.
• CSV generating/loading correctly
• Create data viz based on the sketch.
• Data viz performance evaluation.
• Data viz Interaction
• Finalize the project.

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Distributed under the MIT License. See LICENSE for more information.

Xiaofeng(Stefan) Zhang - [email protected]

Project Link: https://github.com/StefanZhang/TwitterRealTimeAnalysis-Dashboard